Parameter Sensitivity Analysis of Social Spider Algorithm

Social Spider Algorithm (SSA) is a recently proposed general-purpose real-parameter metaheuristic designed to solve global numerical optimization problems. This work systematically benchmarks SSA on a suite of 11 functions with different control parameters. We conduct parameter sensitivity analysis of SSA using advanced non-parametric statistical tests to generate statistically significant conclusion on the best performing parameter settings. The conclusion can be adopted in future work to reduce the effort in parameter tuning. In addition, we perform a success rate test to reveal the impact of the control parameters on the convergence speed of the algorithm

Base Station Switching Problem for Green Cellular Networks with Social Spider Algorithm

With the recent explosion in mobile data, the energy consumption and carbon footprint of the mobile communications industry is rapidly increasing. It is critical to develop more energy-efficient systems in order to reduce the potential harmful effects to the environment. One potential strategy is to switch off some of the under-utilized base stations during off-peak hours. In this paper, we propose a binary Social Spider Algorithm to give guidelines for selecting base stations to switch off. In our implementation, we use a penalty function to formulate the problem and manage to bypass the large number of constraints in the original optimization problem. We adopt several randomly generated cellular networks for simulation and the results indicate that our algorithm can generate superior performance

Carrier Concentration Dependencies of Magnetization & Transport in Ga1-xMnxAs1-yTey

We have investigated the transport and magnetization characteristics of Ga1-xMnxAs intentionally compensated with shallow Te donors. Using ion implantation followed by pulsed-laser melting, we vary the Te compensation and drive the system through a metal-insulator transition (MIT). This MIT is associated with enhanced low-temperature magnetization and an evolution from concave to convex temperature-dependent magnetization.Comment: 2 pages, 2 figures. To appear in the proceedings of the 27th International Conference on the Physics of Semiconductors (ICPS-27, Flagstaff, AZ, July 26-30, 2004

Quantum adaptation of noisy channels

Probabilistic quantum filtering is proposed to properly adapt sequential independent quantum channels in order to stop sudden death of entanglement. In the adaptation, the quantum filtering does not distill or purify more entanglement, it rather properly prepares entangled state to the subsequent quantum channel. For example, the quantum adaptation probabilistically eliminates the sudden death of entanglement of two-qubit entangled state with isotropic noise injected into separate amplitude damping channels. The result has a direct application in quantum key distribution through noisy channels.Comment: 6 pages, 4 figure